Nuclear magnetic resonance (NMR) fingerprinting

ABSTRACT

Apparatus, methods, and other embodiments associated with NMR fingerprinting are described. One example NMR apparatus includes an NMR logic configured to repetitively and variably sample a (k, t, E) space associated with an object to acquire a set of NMR signals. Members of the set of NMR signals are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The varying parameters may include flip angle, echo time, RF amplitude, and other parameters. The NMR apparatus may also include a signal logic configured to produce an NMR signal evolution from the NMR signals, a matching logic configured to compare a signal evolution to a known, simulated or predicted signal evolution, and a characterization logic configured to characterize a resonant species in the object as a result of the signal evolution comparisons.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/051,044 filed Mar. 18, 2011.

BACKGROUND

Conventional magnetic resonance imaging (MRI) pulse sequences include apreparation phase, a waiting phase, and an acquisition phase that areconfigured to produce signals from which images can be made. Thepreparation phase determines when a signal can be acquired anddetermines the properties of the acquired signal. For example, a firstpulse sequence may be designed to produce a T1-weighted signal at afirst echo time (TE) while a second pulse sequence may be designed toproduce a T2-weighted signal at a second TE. However, a lot ofpreparations and a lot of short waits, especially when compounded overmultiple pulse sequences, can add up to a long time to make an image.These conventional pulse sequences are typically designed to providequalitative results where images are acquired with various weightings orcontrasts that highlight a particular parameter (e.g., T1 relaxation, T2relaxation). These conventional pulse sequences are constrained by thefact that conventional MRI is focused mainly on producing images.Constraining NMR to techniques that facilitate producing images when thegoal of an NMR inquiry is to determine relaxation parameters may not beappropriate.

A conventional MRI acquisition involves numerous repetitions ofprepare/wait/acquire pulse sequences. For example, the first pulsesequence may be applied a large number of times to acquire T1 weightedsignals for all voxels in a volume of interest (Rol) and then the secondpulse sequence may be applied a large number of times to acquire T2weighted signals for all the voxels in the Rol. Registering (e.g.,aligning) the signals from these two acquisitions may be difficult.Regardless of how lengthy and how difficult the process, anyone who hashad an MRI on a diseased hip joint knows that this repetitiveapplication of prepare/wait/acquire pulse sequences can produceexcellent and valuable diagnostic images, after about forty-five minutes“in the bore”.

The images are viewed by a radiologist and/or surgeon who interprets thequalitative images for specific disease signatures. The radiologist mayexamine multiple image types (e.g., T1-weighted, T2-weighted) acquiredin multiple imaging planes to make a diagnosis. The radiologist or otherindividual examining the qualitative images may need particular skill tobe able to assess changes from session to session, from machine tomachine, and from machine configuration to machine configuration. Thus,the images are only as good as the image interpreter and all image based(e.g., qualitative) diagnoses end up being subjective.

Seen from a different point of view, conventional MRI uses precisepreparation time to create precise preparation conditions thatfacilitate acquiring precise signals from precise locations at precisepoints in time to make imprecise qualitative images. Conventional MRIattempts to force voxel contents (e.g., water, fat) to emit certainsignals at certain times and then reconstructs images from thesesignals. But forcing Nature may not be the appropriate approach.

Regardless of these shortcomings, conventional MRI has served theclinical community well for many years. However, improved apparatus andmethods may benefit from simply listening to Nature and recalling whatNature has told us in the past, rather than telling Nature when and howto speak.

Attempts have been made to listen to Nature, rather than to coerceNature. For example, Twieg proposed an approach involving compressedsensing where a model of a signal was used to reduce the total amount ofdata needed to reconstruct a parameter map and then to reconstruct animage. Similarly, Doneva et al. proposed random under-sampling toachieve compressed sensing. In the Doneva approach, a pixel willrepresent its true signal evolution plus aliased signal from otherpixels. In one embodiment, the aliasing will only appear as added noiseat a pixel. The noise will not have structure and will not correlate tothe true signal evolution. The Doneva approach facilitates performing arelatively simple process like Orthogonal Matching Pursuit (OMP) toresolve the correct signal to support image reconstruction. OMP assumesthe presence of a constrained dictionary of expected signal evolutions.OMP compares a received signal to the dictionary of signals to identifythe signal that was most likely to come from a pixel.

Twieg, Parsing local signal evolution directly from a single-shot MRIsignal: a new approach for fMRI, Magn Reson Med 2003, November;50(5):1043-52, describes a single-shot MRI method that performssingle-shot parameter assessment by retrieval from signal encoding. TheTwieg method abandons the fundamental simplifying assumption used inconventional MRI methods, that the local intrinsic signal does notchange its amplitude or phase during signal acquisition, even thoughthese changes may be substantial, especially during longer periods usedin single-shot image acquisitions. Twieg recognized that, in reality,local decay and phase evolution occur and therefore modeled each signaldatum as a sample from (k, t) space rather than k-space. Twieg adoptedthe more accurate view that each datum has its own location in a (k, t)space that also reflects another attribute (e.g., relaxation, decay),where t is the elapsed time. While Twieg anticipated improved accuracyand robustness due to the new signal model, intensive reconstructioncomputations limited Twieg's progress.

Doneva, et al., Compressed sensing reconstruction for magnetic resonanceparameter mapping, Magnetic Resonance in Medicine, Volume 64, Issue 4,pages 1114-1120, October 2010, recognizes that different tissues in thehuman body can be distinguished in MRI by their intrinsic MR parametersincluding proton density, longitudinal (T1, spin-lattice) relaxationtime, and transverse (T2, spin-spin) relaxation time. Doneva applies alearned dictionary to sparsify data and then uses a model basedreconstruction for MR parameter mapping. Doneva identifies that“multiple relaxation components in a heterogeneous voxel can beassessed.” However, Doneva uses an imaging based approach that relies ona library whose curves can, in one example, be characterized byequations of the form:SE=1−2e ^(−1/Tx)

where:

-   -   SE is a signal evolution,    -   t is time, and    -   Tx is a single relaxation parameter.

In another, more general example, Doneva uses an imaging based approachthat relies on a library whose curves can be characterized by:SE=A+Be ^(−t/C)

-   -   where A is a constant, B is a constant, t is time, and C is a        single relaxation parameter.

The Doneva library is limited to the idealized, single relaxationparameter curves because the preparation is specific and constrained bythe fact that Doneva ultimately reconstructs an image from the acquireddata. Thus, any variations in t appear to be constant or linear and anyvariations in a also appear to be constant or linear.

While Twieg and Doneva certainly advanced the art, Twieg and Donevaappear to be limited to conventional imaging sequences that highlightonly one or a few parameters. To the extent that Twieg or Doneva use anyquantitative sequences, these sequences include an excitation andpreparation scheme that generates a contrast between different tissueswith different properties. However, the preparation fades over timeuntil no more useful information can be acquired unless preparation isrepeated. For example, after about 4-5 seconds, tissues subjected to aninversion recovery sequence designed for T1 contrast will have recoveredto their equilibrium state and will yield no more signal. This shorttime limit compromises the ability to perform three dimensional imaging,imaging of moving targets, and so on. Additionally, Twieg and Donevaappear further limited to acquiring information associated with onerelaxation parameter at a time. Twieg and Doneva appear suited tocollecting information about T1 relaxation, T2 relaxation, or one fixedcombination of T1 and T2, but not both simultaneously. To the extentthat Twieg and Doneva could acquire information about T1 and T2, thesensitivity to either would be constant through the acquisition.

Therefore, while some attempts have been made to be less coercive insignal acquisition, conventional MRI still attempts to force certainsignals at certain times to support assumptions required in conventionalimage reconstruction. Thus, Nature is still waiting for someone tosimply listen and recall.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example systems, methods,and other example embodiments of various aspects of the invention. Itwill be appreciated that the illustrated element boundaries (e.g.,boxes, groups of boxes, or other shapes) in the figures represent oneexample of the boundaries. One of ordinary skill in the art willappreciate that in some examples one element may be designed as multipleelements or that multiple elements may be designed as one element. Insome examples, an element shown as an internal component of anotherelement may be implemented as an external component and vice versa.Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates a volume that contains two resonant species.

FIG. 2 illustrates two individual NMR signals received from two resonantspecies and a signal evolution derived from the two individual NMRsignals.

FIG. 3 compares and contrasts conventional sequence blocks to examplesequence blocks.

FIG. 4 illustrates an example method associated with NMR fingerprinting.

FIG. 5 illustrates an example method associated with NMR fingerprinting.

FIG. 6 illustrates an example apparatus associated with NMRfingerprinting.

FIG. 7 illustrates an example apparatus associated with NMRfingerprinting.

FIG. 8 illustrates an MRI apparatus configured to perform NMRfingerprinting.

FIG. 9 illustrates another example set of sequence blocks.

DETAILED DESCRIPTION

Example apparatus and methods do not try to force each resonant speciescontaining area in an object to produce a desired signal at a desiredtime in response to specifically crafted preparation/wait/acquiresequences. Example apparatus and methods do not iterate through areas tobe analyzed several times using identical specifically craftedpreparation or excitation schemes to highlight individual parameters.

Instead, example apparatus and methods employ a series of variedsequence blocks that produce different signal evolutions in differentresonant species (e.g., tissues) to which the RF is applied. The term“resonant species”, as used herein, refers to an item (e.g., water, fat,tissue) that can be made to resonate using NMR. By way of illustration,when example apparatus and methods apply RF energy to a volume that hasboth bone and muscle tissue, then both the bone and muscle tissue willproduce an NMR signal. However the “bone signal” and the “muscle signal”will be different. The different signals can be collected over a periodof time to identify a signal evolution for the volume. Resonant speciesin the volume can then be characterized by comparing the signalevolution to known evolutions. The “known” evolutions may be, forexample, simulated evolutions and/or previously acquired evolutions.Characterizing the resonant species can include identifying differentrelaxation parameters (e.g., T1, T2, diffusion resonant frequency).

Example apparatus and methods do not define what the signals produced bythe resonant species must be, only that the signals be different betweendifferent resonant species being examined. Unlike conventional systems,the different NMR signals may not have constant signal strength orphase. Since tissues may produce different signals, the process ofdetermining the relaxation parameters is reduced to pattern recognitionin the signal time course. The pattern recognition may be performedusing, for example, different variations of Orthogonal Matching Pursuit(OMP). Example apparatus and methods facilitate maximizing contrastbetween resonant species without ignoring resonant species that may bein the volume or object. Thus, NMR fingerprinting involves applying aseries of varied sequence blocks that generates a particular signalevolution signature (e.g., fingerprint) that is specific for aparticular combination of parameters and resonant species in a volume.Processing performed on received signals does not involve conventionalreconstruction, but rather involves pattern recognition of thedetermined signal evolution.

To understand the difference between listening to Nature and trying toforce Nature, let's revisit some fundamental basics of NMR. Largerobjects like human bodies are made up of smaller objects like arms andlegs and hips. The smaller objects are in turn made up of smaller partslike skin, muscle, fat, bone, tendon, and prosthetics. These smallerparts are in turn made up of even smaller things like water andminerals. The water and minerals are themselves made up of even smallerthings (e.g., hydrogen, oxygen) which in turn are made up of evensmaller things (e.g., electrons orbiting a nucleus). The nucleus mayinclude a proton that exhibits “spin”. A human body has a large numberof protons and thus a large number of spins.

In the presence of a magnetic field, some of the spins will align in onedirection (e.g., N/S) with respect to that magnetic field while otherspins will align in an opposite direction (e.g., S/N) with respect tothat magnetic field. Conventional MRI manipulates the magnetic field sothat a net alignment in one direction is achieved. Conventional MRIfurther manipulates the magnetic field so that local differences in thefield are achieved to allow spatial encoding. For example, x, y, and zgradients may be applied to create local variations in the largermagnetic field. The local variations allow the excitation of some spinswithout the excitation of other spins. Selective excitation is possiblebecause of the Larmor relationship between magnetic fields and spins.The Larmor relationship describes how the frequency at which spinsaccept RF energy is related to the magnetic field in which the spins arelocated.

With the local variations created, RF energy may be applied to selectedsets of spins associated with a local variation to make those spinsbehave in a certain way. For example, spins may be forced into a highenergy state and forced away from their default alignment. When the RFenergy is removed, the spins may return or may be forced to return totheir default alignment. Different spins may return to their defaultalignment at different rates. Similarly, spins may return to theirdefault alignment for different reasons. As the spins return from theforced alignment to the natural alignment, the spins produce a signalthat can be detected for a short period of time. Conventional systemsare limited by this short period of time and must, therefore, constantlyrepeat the process that tips the spins out of one alignment and intoanother alignment from which they can return and produce signal.

Like conventional MRI, NMR fingerprinting manipulates the magnetic fieldand manipulates the application of RF energy at different frequencies.However, example apparatus and methods use a comprehensive inquisitivesignal acquisition approach as opposed to a one-at-a-time coerciveapproach. In one embodiment, NMR fingerprinting employs pseudo-randomroutines that allow a volume to produce the signal(s) the volume isgoing to produce in response to a variety of changing conditions createdby a variety of changing applications of RF energy. NMR fingerprintingthen compares a signal that evolves from the received signals to knownsignals received from other acquisitions at other times under similarconditions or to a set of simulated expected or predicted curves. If thereceived signal evolution matches or can be fit to within a threshold ofa known, simulated, or predicted signal evolution, then the volume thatgenerated the signal evolution likely holds the same number, type, andmixture of spins as the volume that produced that matched or fittedsignal evolution. If relaxation parameters are available for the fittedor matched signal evolution, then conventional relaxation parameterdeterminations may be skipped.

The frequency at which water in a volume will accept RF energy isdetermined by the magnetic field in which the water is located. Thefrequency can be computed when the magnetic field is known. Thefrequency at which fat in the same volume will accept RF energy is alsodetermined by the magnetic field in which the fat is located. Thisfrequency can also be computed when the magnetic field is known. Thus,applying multiple frequencies can induce multiple resonant species toresonate. Applying the multiple frequencies under a series of differentconditions at different times can cause the resonant species to resonatein different ways. Additionally, applying the multiple frequencies underdifferent conditions at different times can cause the resonant speciesto resonate and relax in different ways. The different resonations anddifferent relaxations may yield a unique signal evolution for acombination of resonant species.

If a volume only has water, then the volume will only produce onesignal. If the volume only has fat, then the volume will also onlyproduce one signal, but it will be a different signal. Different amountsof fat and water in the same volume will yield different signals. Thecombination of signals acquired under different conditions may yieldnearly infinitely unique signal evolutions. While the human body is acomplicated thing, from a certain point of view it is not thatcomplicated. Every volume in a human body can only hold a finite set ofthings arranged in a finite set of ways. Over time, a comprehensivelibrary of signal evolutions associated with many of the most relevantcombinations of resonant species may be acquired and be available to NMRfingerprinting apparatus. The library may store known signals that maybe referred to as baseline signatures or known signal evolutions. Indifferent embodiments, the library may store simulated and/or predictedsignal evolutions. Thus in different examples, “known” signal evolutionsmay include previously acquired signal evolutions and/or simulatedsignal evolutions.

In one embodiment, baseline signatures can be associated with materialsthat were analyzed solely for producing baseline signatures. Forexample, a beaker of water may be analyzed for a period of time usingvaried sequence blocks that produce a signal evolution. Similarly, abeaker of fat, a bone, a prosthetic hip, or other things that resonatemay be analyzed, and signal evolutions retrieved from these items inresponse to applying selected combinations of varied sequence blocksover time under selected combinations of varied conditions. Thesesignals may be used as baseline signatures for other objects that areanalyzed.

In another embodiment, baseline signatures can be acquired from theobject being analyzed. Volumes in the object may be imaged using aconventional technique and may also be subjected to NMR fingerprinting.For example, 1% of a leg may be imaged conventionally and also processedusing example NMR fingerprinting to establish baseline signatures forbone and other tissues. The 1% may be processed to calibrate anapparatus or method. With the calibration and baseline signaturesacquired, the remaining 99% may be analyzed using NMR fingerprintingthat relies on the baseline signatures established by processing the 1%.Even if some volumes produce a signal for which no fingerprinting matchcan be made, those volumes may simply be imaged using a conventionalapproach. Thus, in one embodiment, a combination conventional andfingerprinting approach may be used to establish signatures and forcalibration.

Using pattern matching to compare acquired signal evolutions to knownsignal evolutions may include analyzing a cross-correlation betweensignal evolutions of different tissues acquired using sequence blockshaving different parameters. Ideally, a signal evolution would fit toexactly one member of the multi-dimensional set of known evolutions. Onedimension of the multi-dimensional set could, for example, be associatedwith a first set of acquisition and/or excitation parameters while asecond dimension of the multi-dimensional set could, for example, beassociated with a second set of excitation and/or acquisitionparameters. Over time, the members of the multi-dimensional set could beadapted based on fits that are achieved from live data. Over time,sequence blocks and/or combinations of sequence blocks that yield a moreidentity-matrix like result may be favored over sequence blocks thatyield a matrix with more off-diagonal contributions. This adaptation ofsequence blocks and/or series of sequence blocks based on observedresults may contribute, for example, to calibrating a particular NMRapparatus for MRI fingerprinting.

The following includes definitions of selected terms employed herein.The definitions include various examples and/or forms of components thatfall within the scope of a term and that may be used for implementation.The examples are not intended to be limiting. Both singular and pluralforms of terms may be within the definitions.

References to “one embodiment”, “an embodiment”, “one example”, “anexample”, and so on, indicate that the embodiment(s) or example(s) sodescribed may include a particular feature, structure, characteristic,property, element, or limitation, but that not every embodiment orexample necessarily includes that particular feature, structure,characteristic, property, element or limitation. Furthermore, repeateduse of the phrase “in one embodiment” does not necessarily refer to thesame embodiment, though it may.

“Computer-readable medium”, as used herein, refers to a non-transitorymedium that stores signals, instructions and/or data. Acomputer-readable medium may take forms, including, but not limited to,non-volatile media, and volatile media. Non-volatile media may include,for example, optical disks, magnetic disks, and so on. Volatile mediamay include, for example, semiconductor memories, dynamic memory, and soon. Common forms of a computer-readable medium may include, but are notlimited to, a floppy disk, a flexible disk, a hard disk, a magnetictape, other magnetic medium, an ASIC, a CD, other optical medium, a RAM,a ROM, a memory chip or card, a memory stick, and other media from whicha computer, a processor or other electronic device can read.

“Logic”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, and/or combinations ofeach to perform a function(s) or an action(s), and/or to cause afunction or action from another logic, method, and/or system. Logic mayinclude a software controlled microprocessor, a discrete logic (e.g.,ASIC), an analog circuit, a digital circuit, a programmed logic device,a memory device containing instructions, and so on. Logic may includeone or more gates, combinations of gates, or other circuit components.Where multiple logical logics are described, it may be possible toincorporate the multiple logical logics into one physical logic.Similarly, where a single logical logic is described, it may be possibleto distribute that single logical logic between multiple physicallogics.

An “operable connection”, or a connection by which entities are“operably connected”, is one in which signals, physical communications,and/or logical communications may be sent and/or received. An operableconnection may include a physical interface, an electrical interface,and/or a data interface. An operable connection may include differingcombinations of interfaces and/or connections sufficient to allowoperable control. For example, two entities can be operably connected tocommunicate signals to each other directly or through one or moreintermediate entities (e.g., processor, operating system, logic,software). Logical and/or physical communication channels can be used tocreate an operable connection.

“Signal”, as used herein, includes but is not limited to, electricalsignals, optical signals, analog signals, digital signals, data,computer instructions, processor instructions, messages, a bit, a bitstream, or other means that can be received, transmitted and/ordetected.

“User”, as used herein, includes but is not limited to one or morepersons, software, computers or other devices, or combinations of these.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, determining, and so on, refer to actions and processes of acomputer system, logic, processor, or similar electronic device thatmanipulates and transforms data represented as physical (electronic)quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 1 illustrates a volume 100 (e.g., voxel) that contains two resonantspecies R1 and R2. R1 and R2 may have different relaxation parameters.For example, the T1_(R1) may be less than T1_(R2) while T2_(R1) may begreater than T2_(R2). Conventional systems may acquire a T1 weightedimage and then acquire a T2 weighted image and then register the images.Example apparatus and methods apply RF energy in a series of variedsequence blocks that cause volume 100 to simultaneously producedifferent NMR signals from both R1 and R2. A signal evolution can beproduced from these simultaneously produced different NMR signals.Relaxation parameters (e.g., T1, T2, PD) can be determined from thesignal evolution through pattern matching to other signal evolutions forwhich relaxation parameters are known. The resonant species R1 and R2can then be characterized by the relaxation parameters. Since differenttissues have different known relaxation parameters, different tissuescan be identified using the relaxation parameter characterization. Whiletwo resonant species are illustrated, one skilled in the art willappreciate that a volume may include a greater or lesser number ofresonant species. Therefore, example methods and apparatus apply moregenerally to a volume having multiple resonant species.

FIG. 2 illustrates plots of two individual NMR signals NMR, and NMR₂received from the two resonant species R1 and R2 in volume 100. NMR₁includes data points generated by R1 under different conditions atdifferent times. NMR₂ includes data points generated by R2 under thedifferent conditions at the different times. Signal evolution SE resultsfrom NMR, and NMR₂ being generated and acquired simultaneously. Thespace from which the data points for NMR, and NMR₂ is acquired may bereferred to as a (k, t, E) space, where in different examples, E refersto (T1, T2, D), (T1, T2, D, . . . ), (T1, T2, . . . ) where D refers todiffusion relaxation. In one example, both t and E may be non-linear. Inanother example, both t and E may be pseudo-random. Once again, whiletwo plots associated with two resonant species are illustrated, oneskilled in the art will appreciate that a volume may include a greateror lesser number of resonant species and thus may produce a greater orlesser number of signals. Therefore, example methods and apparatus applymore generally to a volume having one or more resonant species.

FIG. 3 compares and contrasts conventional sequence blocks to examplesequence blocks. Sequence block 300 includes a preparation phase 310 andan acquisition phase 320. During acquisition phase 320, multipleacquisitions using the same flip angle and the same interval betweenacquisitions may be performed. Acquisition phase 320 resembles theDoneva approach, which acquires data from a (k, t) space, where t varieseither constantly or linearly. The constant variation facilitatesacquiring signal with constant amplitude and phase as required forconventional image reconstruction.

Sequence block 330 also includes a phase 340 and an acquisition phase350. Notice that acquisition phase 350 is much longer than acquisitionphase 320. Unlike acquisition phase 320 where parameters are eitherfixed or vary linearly, in acquisition phase 350 the parameters may varywidely, either non-linearly, randomly, and/or pseudo-randomly.Parameters that may vary include, but are not limited to, echo time,flip angle, phase encoding, and others. Note also that while phase 340may, in some examples, be a preparation phase or preparation-like phase,that phase 340 does not necessarily perform a conventional image-centricpreparation.

FIG. 9 illustrates another example set of sequence blocks. In FIG. 9, afirst sequence block SB1 has a first alpha pulse α1 and a series ofidentical α2 pulses. In FIG. 9, a second sequence block SB2 has the samefirst alpha pulse α1 and a different series of identical α2 pulses. Thephase may be the same for the α2 pulses. Thus, in this example, the onlydifference between members of the set of sequence blocks is the numberof α2 pulses. One skilled in the art will appreciate that other sets ofsequence blocks may be employed.

FIG. 4 illustrates a method 400 associated with NMR fingerprinting.Method 400 includes, at 410, controlling an NMR apparatus to apply RFenergy to a volume in an object. The volume may contain one or moreresonant species. In one embodiment, the object may be a human and thusresonant species may include, but are not limited to, tissue, fat,water, hydrogen, and prosthetics.

The RF energy may be applied in a series of variable sequence blocks.Sequence blocks may vary in a number of parameters including, but notlimited to, echo time, flip angle, phase encoding, diffusion encoding,flow encoding, RF pulse amplitude, RF pulse phase, number of RF pulses,type of gradient applied between an excitation portion of a sequenceblock and a readout portion of a sequence block, number of gradientsapplied between an excitation portion of a sequence block and a readoutportion of a sequence block, type of gradient applied between a readoutportion of a sequence block and an excitation portion of a sequenceblock, number of gradients applied between a readout portion of asequence block and an excitation portion of a sequence block, type ofgradient applied during a readout portion of a sequence block, number ofgradients applied during a readout portion of a sequence block, amountof RF spoiling, and amount of gradient spoiling. In differentembodiments two, three, four, or more parameters may vary betweensequence blocks. In different embodiments, the number of parametersvaried between sequence blocks may itself vary. For example, A1(sequence block 1) may differ from A2 in five parameters, A2 may differfrom A3 in seven parameters, and A3 may differ from A4 in twoparameters. One skilled in the art will appreciate that there are anearly infinite number of series of sequence blocks that can be createdby varying this large number of parameters. In one embodiment, a seriesof sequence blocks is crafted so that the series have different amounts(e.g., 1%, 2%, 5%, 10%, 50%, 99%, 100%) of unique sequence blocks asdefined by their varied parameters. In different embodiments, a seriesof sequence blocks may include more than ten, more than one hundred,more than one thousand, more than ten thousand, and more than onehundred thousand sequence blocks. In one example, the only differencebetween consecutive sequence blocks may be the number of α2 pulses asillustrated in FIG. 9.

The RF energy applied during a sequence block is configured to causedifferent individual resonant species to simultaneously produceindividual NMR signals. Unlike conventional systems, at least one memberof the series of variable sequence blocks will differ from at least oneother member of the series of variable sequence blocks in at least Nsequence block parameters, N being an integer greater than one. As notedabove, in different embodiments N may be a number greater than one. Oneskilled in the art will grasp that the signal content of a signalevolution may vary directly with N. Thus, as more parameters are varied,a potentially richer signal is retrieved. Conventionally, a signal thatdepends on a single parameter is desired and required to facilitateimaging. Here, acquiring signals with greater information contentfacilitates producing more distinct and thus more matchable signalevolutions.

In one embodiment, the NMR apparatus may be controlled at 410 to applymembers of the series of variable sequence blocks according to apartially random acquisition plan configured to under-sample the objectat an under-sampling rate R. In different embodiments, rate R may be,for example, two, four, or greater.

Method 400 also includes, at 420, controlling the NMR apparatus toacquire the simultaneously produced individual NMR signals. Unlikeconventional systems where the time during which an imaging-relevant NMRsignal can be acquired is severely limited (e.g., 4-5 seconds), the NMRapparatus can be controlled to acquire NMR signal for significantlylonger periods of time. For example, the NMR apparatus can be controlledto acquire signal for up to ten seconds, for up to twenty seconds, forup to one hundred seconds, or longer. NMR signals can be acquired forlonger periods of time because signal information content remains viablefor longer periods of time in response to the series of varied RF energyapplied at 410. In different embodiments, the information content in thesignal evolution may remain above an information content threshold forat least five seconds, for at least ten seconds, for at least sixtyseconds, or for longer. An information content threshold may describe,for example, the degree to which a subsequent signal acquisitionincludes information that can be retrieved and that differs frominformation acquired in a previous signal acquisition. For example, asignal that has no retrievable information would likely fall below aninformation content threshold while a signal with retrievableinformation that differs from information retrieved from a previoussignal would likely be above the information content threshold.

Method 400 also includes, at 430, controlling the NMR apparatus todetermine a signal evolution from the acquired NMR signals. Determiningthe signal evolution may include storing (k, t, E) space data pointsacquired during action 420. While an individual sequence block may yielda single point in (k, t, E) space, the signal evolution is determined bythe series of variable sequence blocks. Over time, series of variablesequence blocks that yield particularly useful signal evolutions may beidentified.

In one embodiment, the simultaneously produced signals are acquired at420 over a first period of time and the signal evolution is determinedat 430 over a second period of time. In different embodiments the firstperiod of time may be ten seconds or longer, sixty seconds or longer,and even longer. Additionally, in different embodiments, the secondperiod of time may be ten seconds or longer, sixty seconds or longer,and even longer.

Method 400 also includes, at 440, controlling the NMR apparatus tocompare the signal evolution to one or more known, stored, simulated,and/or predicted signal evolutions. In different examples, the “stored”signal evolutions may include previously acquired signals, simulatedsignals, or both. In one embodiment, the stored signal evolutions areassociated with signals not acquired from the object while in anotherembodiment the stored signal evolutions are associated with signalsacquired from the object. In one embodiment, the stored signals may beassociated with signals acquired from the object being analyzed andsignals not acquired from the object being analyzed.

The stored signals may be associated with a potentially very large dataspace. Thus, one skilled in the art will appreciate that the storedsignal evolutions may include signals outside the set of signalevolutions characterized by:SE=A−Be ^(−t/C)

where:

-   -   SE is a signal evolution,    -   A is a constant,    -   B is a constant,    -   t is time, and    -   C is a single relaxation parameter.

Indeed, one skilled in the art will appreciate that the very large dataspace for the stored signal evolutions can be partially described by:

${SE} = {\prod\limits_{i = 1}^{N_{A}}\;{\prod\limits_{j = 1}^{N_{RF}}\;{{R_{i}(\alpha)}{R_{{RF}_{ij}}\left( {\alpha,\varphi} \right)}{R(G)}{E_{i}\left( {{T\; 1},{T\; 2},D} \right)}}}}$

where:

-   -   SE is a signal evolution,    -   N_(A) is a number of sequence blocks,    -   N_(RF) is a number of RF pulses in a sequence block,    -   α is a flip angle,    -   ϕ is a phase angle,    -   Ri(α) is a rotation due to off resonance,    -   R_(RFij)(α, ϕ) is a rotation due to RF differences,    -   R(G) is a rotation due to a gradient,    -   T1 is spin-lattice relaxation,    -   T2 is spin-spin relaxation,    -   D is diffusion relaxation, and    -   E_(i)(T1, T2, D) is decay due to relaxation differences.

While E_(i)(T1, T2, D) is provided as an example, one skilled in the artwill appreciate that in different embodiments, E_(i)(T1, T2, D) mayactually be E_(i)(T1, T2, D, . . . ), or E_(i)(T1, T2, . . . ).

In one example, the summation on j could be replaced by a product on j,eg.:

${SE} = {\prod\limits_{i = 1}^{N_{A}}\;{\prod\limits_{j = 1}^{N_{RF}}\;{{R_{i}(\alpha)}{R_{{RF}_{ij}}\left( {\alpha,\varphi} \right)}{R(G)}{E_{i}\left( {{T\; 1},{T\; 2},D} \right)}}}}$

In NMR, MRI, or ESR (electron spin resonance), a Bloch equation is amember of a set of macroscopic equations that are used to calculate thenuclear magnetization M=(M_(x), M_(y), M_(z)) as a function of time whenrelaxation times T₁ and T₂ are present. These phenomenological equationswere introduced by Felix Bloch and may also be referred to as theequations of motion of nuclear magnetization. One skilled in the artwill appreciate that in one embodiment Ri(α), R_(RFij)(α, ϕ), and R(G)may be viewed as Bloch equations.

While FIG. 4 illustrates various actions occurring in serial, it is tobe appreciated that various actions illustrated in FIG. 4 could occursubstantially in parallel. By way of illustration, a first process couldcontrol applying RF energy, a second process could control acquiring NMRsignals and determining a signal evolution, and a third process couldperform signal evolution comparisons. While three processes aredescribed, it is to be appreciated that a greater and/or lesser numberof processes could be employed.

FIG. 5 illustrates another embodiment of method 400 (FIG. 4). Thisembodiment includes actions 410, 420, 430, and 440. However, thisembodiment also includes actions 412, 414, 416, and 450.

This embodiment of method 400 includes, at 412, controlling the NMRapparatus to vary one or more of, the amount of time between sequenceblocks, the relative amplitude of sequence blocks, and the relativephase of sequence blocks. Thus, not only can the individual parameters(e.g., flip angle, phase) be varied between sequence blocks, but thetimes between sequence blocks and other differences between sequenceblocks can be varied. This facilitates creating additional signalcontent in the signal evolution.

This embodiment of method 400 also includes, at 414, controlling the NMRapparatus to configure a member of the series of variable sequenceblocks as one of, a TrueFISP pulse sequence, a FLASH pulse sequence, anda TSE pulse sequence. Action 414 illustrates that a set of sequenceblocks is not necessarily the same thing as a conventional imaging-basedpulse sequence. A sequence block differs from a conventional pulsesequence for at least the reason that non-linearly varying Δt and ΔE,which produce NMR signals in (k, t, E) space having non-constantamplitudes and phases are encouraged, not prohibited.

This embodiment of method 400 also includes, at 416, controlling the NMRapparatus to configure a later member of the series of variable sequenceblocks based, at least in part, on an NMR signal acquired in response toapplying an earlier member of the series of variable sequence blocks.Thus, this embodiment of method 400 is an adaptive method where theorder of members of the series of varied sequence blocks may not beknown ahead of time. Instead, as data points in (k, t, E) space areacquired, and as a signal evolves, decisions concerning differentsequence blocks and different sets of parameters to vary may be made. Byway of illustration, a first number of data points in (k, t, E) spaceand an evolving signal may be leading towards one relaxation parameterdetermination and away from another relaxation parameter determination.Therefore, sequence blocks that can confirm and/or reject either ofthese leads may be applied next in the series to facilitate a guided andmore rapid convergence in the pattern matching process.

This embodiment of method 400 also includes, at 450, controlling the NMRapparatus to characterize at least one of the resonant species as afunction of comparing the signal evolution to one or more stored (e.g.,known, simulated, predicted) signal evolutions. Comparing the acquiredsignal evolution to a stored signal evolution may include, for example,controlling the NMR apparatus to compare the signal evolution to membersof a multi-dimensional set of NMR signal evolutions. A first dimensionin the multi-dimensional set may be associated with a first set ofsequence block parameters and a second dimension in themulti-dimensional set may be associated with a second, different set ofsequence block parameters. Since a signal evolution evolves over time,the multi-dimensional set may include a time dimension and the patternmatching process may include a path matching process that monitors theprogress of the signal evolution. Additionally, since one series ofvaried sequence blocks may differ from another series of varied sequenceblocks, the multi-dimensional set may include an order dimension whereonce again the pattern matching process may path match as opposed tojust pattern matching.

Characterizing a resonant species may include, for example, identifyingrelaxation parameters including, but not limited to, T1 relaxationassociated with the resonant species, T2 relaxation associated with theresonant species, off-resonance relaxation associated with the resonantspecies, and diffusion weighted relaxation associated with the resonantspecies.

FIG. 6 illustrates an NMR apparatus 600. NMR apparatus 600 includes anNMR logic 610. NMR logic 610 is configured to repetitively and variablysample an object in a (k, t, E) space to acquire a set of NMR signalsthat may have non-constant amplitude and/or phase. Members of the set ofNMR signals are associated with different points in the (k, t, E) space.In different embodiments the different points are sampled according to aplan where t and/or E varies non-linearly and/or in a non-constantmanner.

NMR apparatus 600 also includes a signal logic 620. Signal logic 620 isconfigured to produce an NMR signal evolution from the NMR signals. Thesignal evolution may include a number of NMR signals acquired over aperiod of time.

NMR apparatus 600 also includes a matching logic 630. Matching logic 630is configured to compare the produced NMR signal evolution to a knownNMR signal evolution. The known NMR signal evolution may be, forexample, a previously acquired signal evolution, or a simulated signalevolution.

FIG. 7 illustrates another embodiment of apparatus 600 (FIG. 6). Thisembodiment of apparatus 600 includes a characterization logic 640.Characterization logic 640 is configured to characterize a resonantspecies in the object by comparing the NMR signal evolution to acharacterizing signal evolution(s). Characterizing the resonant speciesmay include identifying relaxation parameters including, but not limitedto, T1 relaxation, T2 relaxation, diffusion weighted relaxation, andoff-resonance relaxation. The characterizing signal evolution(s) may bestored in a library of characterizing signal evolutions.

FIG. 8 illustrates an example MRI apparatus 800 configured with afingerprinting apparatus 899 to facilitate MRI fingerprinting. Thefingerprinting apparatus 899 may be configured with elements of exampleapparatus described herein and/or may perform example methods describedherein.

The apparatus 800 includes a basic field magnet(s) 810 and a basic fieldmagnet supply 820. Ideally, the basic field magnets 810 would produce auniform B₀ field. However, in practice, the B₀ field may not be uniform,and may vary over an object being imaged by the MRI apparatus 800. MRIapparatus 800 may include gradient coils 830 configured to emit gradientmagnetic fields like G_(S), G_(P) and G_(R). The gradient coils 830 maybe controlled, at least in part, by a gradient coils supply 840. In someexamples, the timing, strength, and orientation of the gradient magneticfields may be controlled, and thus selectively adapted, during an MRIprocedure.

MRI apparatus 800 may include a set of RF antennas 850 that areconfigured to generate RF pulses and to receive resulting nuclearmagnetic resonance signals from an object to which the RF pulses aredirected. In some examples, how the pulses are generated and how theresulting MR signals are received may be controlled and thus may beselectively adapted during an MR procedure. Separate RF transmission andreception coils can be employed. The RF antennas 850 may be controlled,at least in part, by a set of RF transmission units 860. An RFtransmission unit 860 may provide a signal to an RF antenna 850.

The gradient coils supply 840 and the RF transmission units 860 may becontrolled, at least in part, by a control computer 870. In one example,the control computer 870 may be programmed to control an NMR device asdescribed herein. Conventionally, the magnetic resonance signalsreceived from the RF antennas 850 can be employed to generate an imageand thus may be subject to a transformation process like a twodimensional FFT that generates pixilated image data. The transformationcan be performed by an image computer 880 or other similar processingdevice. The image data may then be shown on a display 890.

However, fingerprinting apparatus 899 facilitates not having to doconventional reconstruction of an image from MR signals received fromthe RF antennas 850. Thus the RF energy applied to an object byapparatus 800 need not be constrained to produce signals withsubstantially constant amplitudes or phases. Instead, fingerprintingapparatus 899 facilitates matching received signals to known signals forwhich a reconstruction, relaxation parameter, or other information isalready available. This facilitates producing a quantitative result.

While FIG. 8 illustrates an example MRI apparatus 800 that includesvarious components connected in various ways, it is to be appreciatedthat other MRI apparatus may include other components connected in otherways.

While example systems, methods, and so on have been illustrated bydescribing examples, and while the examples have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe systems, methods, and so on described herein. Therefore, theinvention is not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Thus, thisapplication is intended to embrace alterations, modifications, andvariations that fall within the scope of the appended claims.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

To the extent that the phrase “one or more of, A, B, and C” is employedherein, (e.g., a data store configured to store one or more of, A, B,and C) it is intended to convey the set of possibilities A, B, C, AB,AC, BC, and/or ABC (e.g., the data store may store only A, only B, onlyC, A&B, A&C, B&C, and/or A&B&C). It is not intended to require one of A,one of B, and one of C. When the applicants intend to indicate “at leastone of A, at least one of B, and at least one of C”, then the phrasing“at least one of A, at least one of B, and at least one of C” will beemployed.

What is claimed is:
 1. A method, comprising: controlling a nuclearmagnetic resonance (NMR) apparatus to apply radio frequency (RF) energyto a volume in an object in a series of variable sequence blocks, wherea sequence block includes one or more excitation phases, one or morereadout phases, and one or more waiting phases, where the volumecontains one or more resonant species, where the RF energy appliedduring a sequence block is configured to cause the one or more resonantspecies in the volume to simultaneously produce individual NMR signals,and where at least one member of the series of variable sequence blocksdiffers from at least one other member of the series of variablesequence blocks in at least N sequence block parameters, N being aninteger greater than one; controlling the NMR apparatus to acquire thesimultaneously produced individual NMR signals; controlling the NMRapparatus to compare the acquired NMR signals to one or more knownsignal evolutions, and controlling the NMR apparatus to characterize atleast one of the resonant species as a function of comparing theacquired NMR signals to the one or more known signal evolutions, wherecharacterizing the resonant species comprises identifying one or moreof, T1 relaxation associated with the resonant species, T2 relaxationassociated with the resonant species, off-resonance relaxationassociated with the resonant species, and diffusion weighted relaxationassociated with the resonant species, where the known signal evolutionsinclude a signal selected from a set of signals described by:${SE} = {\overset{N_{s}}{\sum\limits_{s = 1}}{\prod\limits_{i = 1}^{N_{A}}\;{\prod\limits_{j = 1}^{N_{RF}}\;{{R_{i}(\alpha)}{R_{{RF}_{ij}}\left( {\alpha,\phi} \right)}{R(G)}{E_{i}\left( {{T\; 1},{T\; 2},D} \right)}M_{0}\mspace{14mu}{or}}}}}$${SE} = {\overset{N_{s}}{\sum\limits_{s = 1}}{\prod\limits_{i = 1}^{N_{A}}\;{\prod\limits_{j = 1}^{N_{RF}}\;{{R_{i}(\alpha)}{R_{{RF}_{ij}}\left( {\alpha,\phi} \right)}{R(G)}{E_{i}\left( {{T\; 1},{T\; 2},D} \right)}M_{0}}}}}$where: SE is a signal evolution, N_(S) is a number of spins, N_(A) is anumber of sequence blocks, N_(RF) is a number of RF pulses in a sequenceblock, α is a flip angle, ϕ is a phase angle, Ri(α) is a rotation due tooff resonance, R_(RFij)(α, ϕ) is a rotation due to RF differences, R(G)is a rotation due to a gradient, T1 is spin-lattice relaxation, T2 isspin-spin relaxation, D is diffusion relaxation, E_(i)(T1, T2, D) isdecay due to relaxation differences, and M₀ is the default or naturalalignment to which spins align when placed in the main magnetic field.2. The method of claim 1, where the sequence block parameters compriseecho time, flip angle, phase encoding, diffusion encoding, flowencoding, RF pulse amplitude, RF pulse phase, number of RF pulses, typeof gradient applied between an excitation portion of a sequence blockand a readout portion of a sequence block, number of gradients appliedbetween an excitation portion of a sequence block and a readout portionof a sequence block, type of gradient applied between a readout portionof a sequence block and an excitation portion of a sequence block,number of gradients applied between a readout portion of a sequenceblock and an excitation portion of a sequence block, type of gradientapplied during a readout portion of a sequence block, number ofgradients applied during a readout portion of a sequence block, amountof RF spoiling, and amount of gradient spoiling.
 3. The method of claim2, where the sequence block parameters are selected from the groupincluding echo time, flip angle, phase encoding, diffusion encoding,flow encoding, RF pulse amplitude, RF pulse phase, number of RF pulses,type of gradient applied between an excitation portion of a sequenceblock and a readout portion of a sequence block, number of gradientsapplied between an excitation portion of a sequence block and a readoutportion of a sequence block, type of gradient applied between a readoutportion of a sequence block and an excitation portion of a sequenceblock, number of gradients applied between a readout portion of asequence block and an excitation portion of a sequence block, type ofgradient applied during a readout portion of a sequence block, number ofgradients applied during a readout portion of a sequence block, amountof RF spoiling, and amount of gradient spoiling.
 4. The method of claim3, comprising: controlling the NMR apparatus to vary one or more of, theamount of time between sequence blocks in the series of variablesequence blocks, the relative amplitude of RF pulses in sequence blocksin the series of variable sequence blocks, and the relative phase of RFpulses in sequence blocks in the series of variable sequence blocks. 5.The method of claim 4, comprising controlling the NMR apparatus toconfigure a member of the series of variable sequence blocks as one of,a TrueFISP pulse sequence, a FLASH pulse sequence, and a TSE pulsesequence.
 6. The method of claim 5, where at least one percent of themembers of the series of variable sequence blocks are unique.
 7. Themethod of claim 6, where at least ten percent of the members of theseries of variable sequence blocks are unique.
 8. The method of claim 7,comprising: controlling the NMR apparatus to configure a later member ofthe series of variable sequence blocks based, at least in part, on anNMR signal acquired in response to applying an earlier member of theseries of variable sequence blocks.
 9. The method of claim 8, the objectbeing an animal and the one or more resonant species comprising ananimal tissue, water, and fat.
 10. The method of claim 9, comprising:controlling the NMR apparatus to apply members of the series of variablesequence blocks according to a partially random acquisition planconfigured to under-sample the object at an under-sampling rate R. 11.The method of claim 10, where the rate R is greater than two.
 12. Themethod of claim 1, where the signal evolutions include signals outsidethe set of signal evolutions characterized by:Se=A−Be ^(−t/C) where: SE is a signal evolution, A is a constant, B is aconstant, t is time, and C is a single relaxation parameter.
 13. Themethod of claim 1, where controlling the NMR apparatus to characterizeat least one of the resonant species as a function of comparing theacquired NMR signals to one or more known signal evolutions comprises:controlling the NMR apparatus to compare the acquired NMR signals tomembers of a multi-dimensional set of NMR signal evolutions, where afirst dimension in the multi-dimensional set is associated with a firstset of sequence block parameters, and where a second dimension in themulti-dimensional set is associated with a second, different set ofsequence block parameters.
 14. The method of claim 1, where signalcontent of the acquired NMR signals varies directly with N.
 15. Themethod of claim 1, where information content in the acquired NMR signalsremains above an information content threshold for at least 5 seconds.